Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks Article Swipe
Yue Yu
,
Xuan Kan
,
Hejie Cui
,
Ran Xu
,
Yujia Zheng
,
Xiangchen Song
,
Yanqiao Zhu
,
Kun Zhang
,
Razieh Nabi
,
Ying Guo
,
Chao Zhang
,
Carl Yang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.00261
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.00261
Functional magnetic resonance imaging (fMRI) has become one of the most common imaging modalities for brain function analysis. Recently, graph neural networks (GNN) have been adopted for fMRI analysis with superior performance. Unfortunately, traditional functional brain networks are mainly constructed based on similarities among region of interests (ROI), which are noisy and agnostic to the downstream prediction tasks and can lead to inferior results for GNN-based models. To better adapt GNNs for fMRI analysis, we propose TBDS, an end-to-end framework based on \underline{T}ask-aware \underline{B}rain connectivity \underline{D}AG (short for Directed Acyclic Graph) \underline{S}tructure generation for fMRI analysis. The key component of TBDS is the brain network generator which adopts a DAG learning approach to transform the raw time-series into task-aware brain connectivities. Besides, we design an additional contrastive regularization to inject task-specific knowledge during the brain network generation process. Comprehensive experiments on two fMRI datasets, namely Adolescent Brain Cognitive Development (ABCD) and Philadelphia Neuroimaging Cohort (PNC) datasets demonstrate the efficacy of TBDS. In addition, the generated brain networks also highlight the prediction-related brain regions and thus provide unique interpretations of the prediction results. Our implementation will be published to https://github.com/yueyu1030/TBDS upon acceptance.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2211.00261
- https://arxiv.org/pdf/2211.00261
- OA Status
- green
- Cited By
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4308020307
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4308020307Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2211.00261Digital Object Identifier
- Title
-
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural NetworksWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-11-01Full publication date if available
- Authors
-
Yue Yu, Xuan Kan, Hejie Cui, Ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl YangList of authors in order
- Landing page
-
https://arxiv.org/abs/2211.00261Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2211.00261Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2211.00261Direct OA link when available
- Concepts
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Human Connectome Project, Computer science, Functional magnetic resonance imaging, Neuroimaging, Artificial intelligence, Connectome, Machine learning, Independent component analysis, Functional connectivity, Pattern recognition (psychology), Neuroscience, PsychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
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2025: 1, 2024: 2, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(ABCD) | 149 |
| abstract_inverted_index.(ROI), | 47 |
| abstract_inverted_index.(fMRI) | 4 |
| abstract_inverted_index.(short | 86 |
| abstract_inverted_index.Cohort | 153 |
| abstract_inverted_index.Graph) | 90 |
| abstract_inverted_index.adopts | 107 |
| abstract_inverted_index.become | 6 |
| abstract_inverted_index.better | 68 |
| abstract_inverted_index.common | 11 |
| abstract_inverted_index.design | 123 |
| abstract_inverted_index.during | 132 |
| abstract_inverted_index.inject | 129 |
| abstract_inverted_index.mainly | 38 |
| abstract_inverted_index.namely | 144 |
| abstract_inverted_index.neural | 20 |
| abstract_inverted_index.region | 44 |
| abstract_inverted_index.unique | 176 |
| abstract_inverted_index.Acyclic | 89 |
| abstract_inverted_index.adopted | 25 |
| abstract_inverted_index.imaging | 3, 12 |
| abstract_inverted_index.models. | 66 |
| abstract_inverted_index.network | 104, 135 |
| abstract_inverted_index.propose | 75 |
| abstract_inverted_index.provide | 175 |
| abstract_inverted_index.regions | 172 |
| abstract_inverted_index.results | 63 |
| abstract_inverted_index.Besides, | 121 |
| abstract_inverted_index.Directed | 88 |
| abstract_inverted_index.agnostic | 52 |
| abstract_inverted_index.analysis | 28 |
| abstract_inverted_index.approach | 111 |
| abstract_inverted_index.datasets | 155 |
| abstract_inverted_index.efficacy | 158 |
| abstract_inverted_index.function | 16 |
| abstract_inverted_index.inferior | 62 |
| abstract_inverted_index.learning | 110 |
| abstract_inverted_index.magnetic | 1 |
| abstract_inverted_index.networks | 21, 36, 166 |
| abstract_inverted_index.process. | 137 |
| abstract_inverted_index.results. | 181 |
| abstract_inverted_index.superior | 30 |
| abstract_inverted_index.Cognitive | 147 |
| abstract_inverted_index.GNN-based | 65 |
| abstract_inverted_index.Recently, | 18 |
| abstract_inverted_index.addition, | 162 |
| abstract_inverted_index.analysis, | 73 |
| abstract_inverted_index.analysis. | 17, 95 |
| abstract_inverted_index.component | 98 |
| abstract_inverted_index.datasets, | 143 |
| abstract_inverted_index.framework | 79 |
| abstract_inverted_index.generated | 164 |
| abstract_inverted_index.generator | 105 |
| abstract_inverted_index.highlight | 168 |
| abstract_inverted_index.interests | 46 |
| abstract_inverted_index.knowledge | 131 |
| abstract_inverted_index.published | 186 |
| abstract_inverted_index.resonance | 2 |
| abstract_inverted_index.transform | 113 |
| abstract_inverted_index.Adolescent | 145 |
| abstract_inverted_index.Functional | 0 |
| abstract_inverted_index.additional | 125 |
| abstract_inverted_index.downstream | 55 |
| abstract_inverted_index.end-to-end | 78 |
| abstract_inverted_index.functional | 34 |
| abstract_inverted_index.generation | 92, 136 |
| abstract_inverted_index.modalities | 13 |
| abstract_inverted_index.prediction | 56, 180 |
| abstract_inverted_index.task-aware | 118 |
| abstract_inverted_index.Development | 148 |
| abstract_inverted_index.acceptance. | 190 |
| abstract_inverted_index.constructed | 39 |
| abstract_inverted_index.contrastive | 126 |
| abstract_inverted_index.demonstrate | 156 |
| abstract_inverted_index.experiments | 139 |
| abstract_inverted_index.time-series | 116 |
| abstract_inverted_index.traditional | 33 |
| abstract_inverted_index.Neuroimaging | 152 |
| abstract_inverted_index.Philadelphia | 151 |
| abstract_inverted_index.connectivity | 84 |
| abstract_inverted_index.performance. | 31 |
| abstract_inverted_index.similarities | 42 |
| abstract_inverted_index.Comprehensive | 138 |
| abstract_inverted_index.task-specific | 130 |
| abstract_inverted_index.Unfortunately, | 32 |
| abstract_inverted_index.implementation | 183 |
| abstract_inverted_index.regularization | 127 |
| abstract_inverted_index.\underline{D}AG | 85 |
| abstract_inverted_index.connectivities. | 120 |
| abstract_inverted_index.interpretations | 177 |
| abstract_inverted_index.\underline{B}rain | 83 |
| abstract_inverted_index.prediction-related | 170 |
| abstract_inverted_index.\underline{S}tructure | 91 |
| abstract_inverted_index.\underline{T}ask-aware | 82 |
| abstract_inverted_index.https://github.com/yueyu1030/TBDS | 188 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 12 |
| citation_normalized_percentile |